docs: def gini(data):
      """
      Calculate the `Gini coefficient
      <https://en.wikipedia.org/wiki/Gini_coefficient>`_ of a 2D array.
  
      The Gini coefficient is calculated using the prescription from `Lotz
      et al. 2004 <http://adsabs.harvard.edu/abs/2004AJ....128..163L>`_
      as:
  
      .. math::
          G = \\frac{1}{\\left | \\bar{x} \\right | n (n - 1)}
          \\sum^{n}_{i} (2i - n - 1) \\left | x_i \\right |
  
      where :math:`\\bar{x}` is the mean over all pixel values
      :math:`x_i`.
  
      The Gini coefficient is a way of measuring the inequality in a given
      set of values. In the context of galaxy morphology, it measures how
      the light of a galaxy image is distributed among its pixels.  A
      ``G`` value of 0 corresponds to a galaxy image with the light evenly
      distributed over all pixels while a ``G`` value of 1 represents a
      galaxy image with all its light concentrated in just one pixel.
  
      Usually Gini's measurement needs some sort of preprocessing for
      defining the galaxy region in the image based on the quality of the
      input data. As there is not a general standard for doing this, this
      is left for the user.
  
      Parameters
      ----------
      data : array-like
          The 2D data array or object that can be converted to an array.
  
      Returns
      -------
      gini : `float`
          The Gini coefficient of the input 2D array.
      """
  
      flattened = np.sort(np.ravel(data))
      N = np.size(flattened)
      normalization = 1. / (np.abs(np.mean(flattened)) * N * (N - 1))
      kernel = (2 * np.arange(1, N + 1) - N - 1) * np.abs(flattened)
      G = normalization * np.sum(kernel)
  
      return G